Scheduling with artificial neural networks

Abstract

Artificial Neural Networks (ANNs) attempt to emulate the massively parallel and distributed processing of the human brain. They are being examined for a variety of problems that have been very difficult to solve. The objective of this thesis is to review the current applications of ANNs to scheduling problems and to develop a parallelized network model for solving the single machine mean tardiness scheduling problem and the problem of finding the minimum makespan in a job-shop. The proposed model is also compared with the existing heuristic procedures under a variety of experimental conditions.